A novel framework for road traffic risk assessment with HMM-based prediction model

Xunjia Zheng, Di Zhang, Hongbo Gao, Zhiguo Zhao, Heye Huang, Jianqiang Wang

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

Over the past decades, there has been significant research effort dedicated to the development of intelligent vehicles and V2X systems. This paper proposes a road traffic risk assessment method for road traffic accident prevention of intelligent vehicles. This method is based on HMM (Hidden Markov Model) and is applied to the prediction of steering angle status to (1) evaluate the probabilities of the steering angle in each independent interval and (2) calculate the road traffic risk in different analysis regions. According to the model, the road traffic risk is quantified and presented directly in a visual form by the time-varying risk map, to ensure the accuracy of assessment and prediction. Experiment results are presented, and the results show the effectiveness of the assessment strategies.

Original languageEnglish
Article number4313
JournalSensors (Switzerland)
Volume18
Issue number12
DOIs
Publication statusPublished - 2018 Dec

Keywords

  • Hidden markov model
  • Intelligent transportation system
  • Road traffic risk assessment
  • V2X

ASJC Scopus subject areas

  • Analytical Chemistry
  • Biochemistry
  • Atomic and Molecular Physics, and Optics
  • Instrumentation
  • Electrical and Electronic Engineering

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